Vinod Nair

Artificial Intelligence · 2009–2020

A researcher in the Science corpus: 4 papers; trusts 148 researchers and is trusted by 1,564 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Management Science and Operations Research — 7 citations into it
• Model Architectures — 6 citations into it; #21 of 2,278 by trust here, #82 by citations
• Signal Processing — 6 citations into it
• Generative Models — 5 citations into it

Whom Vinod Nair trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Yann LeCun
3. Yoshua Bengio
4. Hugo Larochelle
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Ruslan Salakhutdinov
8. L. Eon Bottou
9. Kaiming He
10. Max Welling

Who trusts Vinod Nair overall

1. Yoshua Bengio
2. Kaiming He
3. Ruslan Salakhutdinov
4. Quoc V. Le
5. Xiaogang Wang
6. Sergey Levine
7. Dacheng Tao
8. Trevor Darrell
9. Alan L. Yuille
10. Ross B. Girshick

Papers from Vinod Nair's perspective

Ordered by how strongly the researchers Vinod Nair trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Solving Mixed Integer Programs Using Neural Networks (2020) — 322 citations
• Learning Fast Optimizers for Contextual Stochastic Integer Programs (2018) — 23 citations
• Rectified Linear Units Improve Restricted Boltzmann Machines (2010) — 18,880 citations
• 3D Object Recognition with Deep Belief Nets (2009) — 370 citations